Description of the Prior Art
Acoustic noise suppression systems generally serve the purpose of improving overall quality of the desired signal by distinguishing the signal from the ambient background noise.
Earlier noise suppression systems have used spectral substraction techniques and gain modification techniques in an effort to optimize noise suppression. In those approaches, the audio input signal is divided into spectral bands by a bank of bandpass filters, and particular spectral bands are attenuated using gain estimators to reduce their noise energy content.
In most prior art techniques, in order to apply the proper gain factor it is necessary to estimate the energy content of the current background noise present as accurately as possible.
Numerous approaches have been attempted to accurately estimate the current noise but have met limited success. For example, earlier data processing systems appear to have generally used feed forward systems. Those systems have been limited in the accuracy of their noise estimates because they have relied primarily on the energy in current (present-time) signals in order to generate their noise estimates.
Later digital signal processing systems have adopted more sophisticated estimating techniques. For example, a system which utilizes a minimum mean-square error short time spectral amplitude estimator is discussed by Ephraim and Malah. That approach results in a significant reduction in noise and provides enhanced speech with colorless noise. Subsequent work along these lines has produced an error estimation technique that minimizes the mean-square error of the long-spectra.
Those estimators have been found to lower the residual noise level without further affecting the speech itself. However, those estimation techniques in and of themselves have been unable to remove colorless background noise. Moreover, those estimating techniques are essentially mathematical, and the way they are implemented critically affects their effectiveness within a total noise reduction system. Further, those approaches do not appear to rely on previously processed results but essentially rely on current noisy speech signals.
Systems that have used previously processed signal information have generally been unsophisticated and have avoided sophisticated processing techniques. One such system, taught by Borth, in U.S. Pat. No. 4,628,529, uses the occurrence of minima in the post-processed signal energy in order to control the time at which the background noise measurement is estimated. Specifically, Borth discloses a recursive filter which uses the time averaged value of each speech energy estimate for making a speech/noise decision in performing the background noise estimation. However, the Borth invention was designed to operate in a high noise background and was not adapted for implementation using sophisticated digital signal processing.
In addition, Borth and the other prior art systems have generally focused on accurately estimating either the gain factor or the signal to noise ratio (SNR) of the background noise estimator alone and have not used previously computed estimators or prior instantaneous speech signals at every estimator stage.
Thus, what is needed is a noise reduction system that is useful for high speed digital signal processing and which can cope with time varying noise and various types of noise, including colored noise and white noise, by efficiently using all available noise and speech information. Moreover, what is also needed is a noise reduction system that shows excellent performance over a wide range of signal to noise ratios and is not limited to high background noise applications. What is also needed is a noise reduction system that affords algorithms for deriving more accurate estimators using previous as well as current data. Further, what is desired is a noise reduction system that simultaneously optimizes every estimation step, including the signal to noise ratio, the gain, and the amplitude estimation.